Find research datasets worth reusing

opencc-by-4.0Oct 2024View details →

Data for The Disparities and Development Trajectories of Nations in Achieving the Sustainable Development Goals

This dataset provides the source data for Tables and Figures in the main text and the supplementary information, and the code for the main figure of the article.

opencc-by-4.0Apr 2022View details →

Maps of the Sustainable Development Goal (SDG) indicator 15.3.1 with its sub-indicators for the entire Amazon River Basin

Maps of the SDG indicator 15.3.1 adopted by the United Nations Convention to Combat Desertification (UNCCD) together with its sub-indicators for the Amazon River Basin for the period 2001-2020. The sub-indicators are trajectory (or trend), state, and performance. The SDG indicator 15.3.1 was calculated using the procedures described in the second version of the Good Practice Guidance for SDG Indicator 15.3.1. The annual LCLU maps from the MapBiomas project at 30 m spatial resolution and the 16-day MOD13Q1 NDVI and SoilGrids dataset were used as inputs. In addition, annualized maps of drought severity derived from SPI12, SPEI12, and scPDSI are added.      A total of seven GeoTIFF files in Geographic Tagged Image File Format (GeoTIFF) format are provided at 250 m spatial resolution. Coding for the SDG indicator 15.3.1, trajectory, state, and performance. -32768   is ‘No data’ -1 is ‘Degraded’ 0 is ‘Stable’’ 1 is ‘Improvement’ Coding for the drought severity. From 0 (minimum drought severity) to 1 (maximum drought severity).

opencc-by-4.0Aug 2021View details →

DOI's with SDG labels on Target level | 1.4M research articles (2009-2020) related to Sustainable Development Goals

Table content: This data set contains 1.4 million publication DOI's related to the <a href="http://metadata.un.org/sdg/">Targets of the Sustainable Development Goals</a> in the period 2009 - 2020. Table dimensions: rows: 1.4 million, columns: 4 / rows: 1.4 million, columns: 180 Table columns: <a href="https://en.wikipedia.org/wiki/Digital_object_identifier">doi</a> | date | <a href="http://metadata.un.org/sdg/ontology#Target">sdg_target</a> | <a href="http://metadata.un.org/sdg/ontology#Goal">sdg_goal</a> / <a href="https://en.wikipedia.org/wiki/Digital_object_identifier">doi</a> | date | <a href="http://metadata.un.org/sdg/ontology#Target">169 sdg_targets</a> | <a href="http://metadata.un.org/sdg/ontology#Goal">17 sdg_goalsl</a> Table formats:  <a href="https://en.wikipedia.org/wiki/Comma-separated_values">.csv</a> | <a href="https://en.wikipedia.org/wiki/Microsoft_Excel">.xlsx</a> | <a href="https://en.wikipedia.org/wiki/Apache_Parquet">.parquet</a> How we made this data: We have made a search on <a href="https://scopus.com">Scopus </a>using the <a href="https://aurora-network-global.github.io/sdg-queries/">Aurora SDG queries version 5</a> for each of the targets, with a limited year range from 2009 till 2020. Good to know: don't be alarmed if you can find a doi that is labeled with more than one target (~16%). This is not a bug, this is a feature... We used 169 queries, one for each target, a publication can appear in more han one result set. Read this <a href="https://zenodo.org/record/4964606/files/Evaluation_on_accuracy_of_mapping_science_to_the_United_Nations__Sustainable_Development_Goals__SDGs__of_the_Aurora_SDG_queries.pdf?download=1">report to learn more about the accuracy</a> of the queries and the data result sets. How can you use this data: You can use this data to 1. quickly match your existing publication lists to this list to see how that your publications are related to the targets of the SDG's. 2. use these as a basis / seed set / gold set to train more advanced text / graph classifiers (after you have extracted title, abstract or even full-text using crossref.org, unpaywall.org, etc) How can you help: <a href="https://sites.google.com/vu.nl/aurora-sdg-research-dashboard/sdg-knowledge-base#h.d2pd3c39k276">Let us know</a> how you use this data. We'll put your project on the list in our <a href="https://sites.google.com/vu.nl/aurora-sdg-research-dashboard/sdg-knowledge-base">SDG matching knowledge base.</a>

opencc-by-4.0Nov 2022View details →

Sustainable Development Goals (SDG) in citizen science - Dataset

The assignment results of SDGs to CS project descriptions are provided in the following dataset. The analysis was conducted based on data retrieved from the CSTRack database on 2022/09/15.  See further detail about the study in D2.2 section 7.3. Content and grouping:  <ul> <li> The dataset contains the following details: Platform ID (from which platform the CS project descriptions were retrieved),  Project Title (Name of the CS project), SDG assignment results (More details about the assignment technique can be found in D3.2 ‘Web Analytics Toolset and Workbench’ - ESA backend), SDG assignment reported in section 7.2 of D2.2 (which only considered the SDG assignment with the highest similarity). </li> </ul>

opencc-by-4.0Sep 2022View details →

Data for 'Nature's contributions to people and the Sustainable Development Goals in Nepal'

Title: Data for 'Nature's contributions to people and the Sustainable Development Goals in Nepal' Recommended Citation: Adhikari, B., Prescott, G., Urbach, D., Chettri, N., & Fischer, M. (2022). Nature’s contributions to people and the sustainable development goals in Nepal. Environmental Research Letters. https://doi.org/10.1088/1748-9326/ac8e1e Principal Investigator: Markus Fischer (markus.fischer@ips.unibe.ch) Authors: Biraj Adhikari (biraj.adhikari@ips.unibe.ch, ORCID: 0000-0002-4260-8706) Graham W Prescott (graham.prescott.research@gmail.com, ORCID:0000-0001-5123-514X) Davnah Urbach (davnah.payne@ips.unibe.ch, ORCID: 0000-0001-9170-7834) Nakul Chettri (nakul.chettri@icimod.org, ORCID: 0000-0002-3338-8879) Markus Fischer (markus.fischer@ips.unibe.ch, ORCID: 0000-0002-5589-5900) Date of data collection: November 2020 - August 2021 Location of data collection: Kathmandu, Nepal Article title: 'Nature's contributions to people and the Sustainable Development Goals in Nepal' R code available at: https://github.com/biraj-ad/r4dLiteratureReview_GithubRep Data Overview: 1. 'Literature_References.csv' List of 119 peer-reviewed journals and 21 grey literature documents used in the review. Each is assigned a unique identifier ("SN Ref") to link it to the other files. 2. 'Drivers_and_Trends.csv' The "Quote" column is the text from papers which has information on (i) trends in ecosystems or NCPs, and if available (ii) direct and/or indirect drivers causing the trends. The "Nature" column indicates which ecosystem (Forest, Farmland, Freshwater, Grassland, Others, and Directly to NCP), the "NCP" column indicates which NCP (categorized into 18 categories based on the IPBES classification) the text refers to. The "NCP Category" column indicates whether the said NCP is a regulating, material or non-material NCP.    We also categorized direct and indirect drivers based on the IPBES classification (Direct Drivers: Land-use Change, Climate Change, Direct Exploitation, Invasive Alien Species, and Pollution; Indirect Drivers: Institutions and Governance, Demographic and Sociocultural, Economic and Technological).     If there were more than one drivers of change for a particular NCP, we have included them in additional columns. In order to avoid multiple counts for trends of a particular NCP, we introduced the "TrendCount" column. For example, columns 3, 4 and 5 refers to the same trend of decreasing WQN, but has 3 Direct Drivers. Therefore, each TrendCount is given a weight of 0.33 so that the total trend adds upto 1. 3. 'NCP_to_SDG.csv' The "Quote" Column is the text from papers which has information on which NCP is contributing towards which SDG. "Remarks from text" are the authors' own remarks based on the text and the overall context of the article. The contribution of NCPs are classified as positive or negative, and indicated in the column "Effect (pos/neg)" The "NCP" column indicates which NCP (categorized into 18 categories according to IPBES) the text refers to, while the "Contribution to SDG" column indicates which SDG the NCP is contributing towards. The "Ecosystem" column indicates which ecosystem (Forest, Farmland, Freshwater, Grassland, Others) the NCP is being supplied from. Codes for NCPS: HAB (Habitat Creation and Maintenance), POL (Pollination and dispersal of seeds and other propagules), AIR (Regulation of Air Quality), CLI (Regulation of Climate), WQN (Regulation of Freshwater Quantity, Location, and Timing), WQL (Regulation of Freshwater and Coastal Water Quality), SOI (Formation, Protection, and Decontamination of Soils and Sediments), HAZ (Regulation of Hazards and Extreme Events), ORG (Regulation of Organisms Detrimental to Humans), NRG (Energy), FOD (Food and Feed), MAT (Materials and Assistance), MED (Medicinal, Biochemical, and Genetic Resources), INS (Learning and Inspiration), EXP (Physical and Psychological Experiences), IDE (Supporting Identities), and OPT (Maintenance of Options). 4. 'Data_consolidated.xlsx' The above three data files combined into one xlsx document

opencc-by-4.0May 2020View details →

Survey data of "Mapping Research Output to the Sustainable Development Goals (SDGs)"

This dataset contains information on what papers and concepts researchers find relevant to map domain specific research output to the 17 Sustainable Development Goals (SDGs). <a href="https://sustainabledevelopment.un.org/sdgs">Sustainable Development Goals</a> are the 17 global challenges set by the United Nations. Within each of the goals specific targets and indicators are mentioned to monitor the progress of reaching those goals by 2030. In an effort to capture how research is contributing to move the needle on those challenges, we earlier have made an initial classification model than enables to quickly identify what research output is related to what SDG. (This <a href="https://aurora-network.global/project/sdg-analysis-bibliometrics-relevance/">Aurora SDG dashboard</a> is the initial outcome as proof of practice.) In order to validate our current classification model (on soundness/precision and completeness/recall), and receive input for improvement, a survey has been conducted to capture expert knowledge from senior researchers in their research domain related to the SDG. The survey was open to the world, but mainly distributed to researchers from the <a href="https://aurora-network.global/">Aurora Universities Network</a>. The survey was open from October 2019 till January 2020, and captured data from 244 respondents in Europe and North America. 17 surveys were created from a single template, where the content was made specific for each SDG. Content, like a random set of publications, of each survey was ingested by a data provisioning server. That collected research output metadata for each SDG in an earlier stage. It took on average 1 hour for a respondent to complete the survey. The outcome of the survey data can be used for validating current and optimizing future SDG classification models for mapping research output to the SDGs. The survey contains the following questions (see inside dataset for exact wording): <ul> <li>Are you familiar with this SDG? <ul> <li>Respondents could only proceed if they were familiar with the targets and indicators of this SDG. Goal of this question was to weed out un knowledgeable respondents and to increase the quality of the survey data.</li> </ul> </li> <li>Suggest research papers that are relevant for this SDG (upload list) <ul> <li>This question, to provide a list, was put first to reduce influenced by the other questions. Goal of this question was to measure the completeness/recall of the papers in the result set of our current classification model. (To lower the bar, these lists could be provided by either uploading a file from a reference manager (preferred) in .ris of bibtex format, or by a list of titles. This heterogenous input was processed further on by hand into a uniform format.)</li> </ul> </li> <li>Select research papers that are relevant for this SDG (radio buttons: accept, reject) <ul> <li>A randomly selected set of 100 papers was injected in the survey, out of the full list of thousands of papers in the result set of our current classification model. Goal of this question was to measure the soundness/precision of our current classification model.</li> </ul> </li> <li>Select and Suggest Keywords related to SDG (checkboxes: accept | text field: suggestions) <ul> <li>The survey was injected with the top 100 most frequent keywords that appeared in the metadata of the papers in the result set of the current classification model. respondents could select relevant keywords we found, and add ones in a blank text field. Goal of this question was to get suggestions for keywords we can use to increase the recall of relevant papers in a new classification model.</li> </ul> </li> <li>Suggest SDG related glossaries with relevant keywords (text fields: url) <ul> <li>Open text field to add URL to lists with hundreds of relevant keywords related to this SDG. Goal of this question was to get suggestions for keywords we can use to increase the recall of relevant papers in a new classification model.</li> </ul> </li> <li>Select and Suggest Journals fully related to SDG (checkboxes: accept | text field: suggestions) <ul> <li>The survey was injected with the top 100 most frequent journals that appeared in the metadata of the papers in the result set of the current classification model. Respondents could select relevant journals we found, and add ones in a blank text field. Goal of this question was to get suggestions for complete journals we can use to increase the recall of relevant papers in a new classification model.</li> </ul> </li> <li>Suggest improvements for the current queries (text field: suggestions per target) <ul> <li>We showed respondents the queries we used in our current classification model next to each of the targets within the goal. Open text fields were presented to change, add, re-order, delete something (keywords, boolean operators, etc. ) in the query to improve it in their opinion. Goal of this question was to get suggestions we can use to increase the recall and precision of relevant papers in a new classification model.</li> </ul> </li> </ul> In the dataset root you'll find the following folders and files: <ul> <li>/00-survey-input/ <ul> <li>This contains the survey questions for all the individual SDGs. It also contains lists of EIDs categorised to the SDGs we used to make randomized selections from to present to the respondents.</li> </ul> </li> <li>/01-raw-data/ <ul> <li>This contains the raw survey output. (Excluding privacy sensitive information for public release.) This data needs to be combined with the data on the provisioning server to make sense.</li> </ul> </li> <li>/02-aggregated-data/ <ul> <li>This data is where individual responses are aggregated. Also the survey data is combined with the provisioning server, of all sdg surveys combined, responses are aggregated, and split per question type.</li> </ul> </li> <li>/03-scripts/ <ul> <li>This contains scripts to split data, and to add descriptive metadata for text analysis in a later stage.</li> </ul> </li> <li>/04-processed-data/ <ul> <li>This is the main final result that can be used for further analysis. Data is split by SDG into subdirectories, in there you'll find files per question type containing the aggregated data of the respondents.</li> </ul> </li> <li>/images/ <ul> <li>images of the results used in this README.md.</li> </ul> </li> <li>LICENSE.md <ul> <li>terms and conditions for reusing this data.</li> </ul> </li> <li>README.md <ul> <li>description of the dataset; each subfolders contains a README.md file to futher describe the content of each sub-folder.</li> </ul> </li> </ul> In the /04-processed-data/ you'll find in each SDG sub-folder the following files.: <ul> <li>SDG-survey-questions.pdf <ul> <li>This file contains the survey questions</li> </ul> </li> <li>SDG-survey-questions.doc <ul> <li>This file contains the survey questions</li> </ul> </li> <li>SDG-survey-respondents-per-sdg.csv <ul> <li>Basic information about the survey and responses</li> </ul> </li> <li>SDG-survey-city-heatmap.csv <ul> <li>Origin of the respondents per SDG survey</li> </ul> </li> <li>SDG-survey-suggested-publications.txt <ul> <li>Formatted list of research papers researchers have uploaded or listed they want to see back in the result-set for this SDG.</li> </ul> </li> <li>SDG-survey-suggested-publications-with-eid-match.csv <ul> <li>same as above, only matched with an EID. EIDs are matched my Elsevier's internal fuzzy matching algorithm. Only papers with high confidence are show with a match of an EID, referring to a record in Scopus.</li> </ul> </li> <li>SDG-survey-selected-publications-accepted.csv <ul> <li>Based on our previous result set of papers, researchers were presented random samples, they selected papers they believe represent this SDG. (TRUE=accepted)</li> </ul> </li> <li>SDG-survey-selected-publications-rejected.csv <ul> <li>Based on our previous result set of papers, researchers were presented random samples, they selected papers they believe not to represent this SDG. (FALSE=rejected)</li> </ul> </li> <li>SDG-survey-selected-keywords.csv <ul> <li>Based on our previous result set of papers, we presented researchers the keywords that are in the metadata of those papers, they selected keywords they believe represent this SDG.</li> </ul> </li> <li>SDG-survey-unselected-keywords.csv <ul> <li>As "selected-keywords", this is the list of keywords that respondents have not selected to represent this SDG.</li> </ul> </li> <li>SDG-survey-suggested-keywords.csv <ul> <li>List of keywords researchers suggest to use to find papers related to this SDG</li> </ul> </li> <li>SDG-survey-glossaries.csv <ul> <li>List of glossaries, containing keywords, researchers suggest to use to find papers related to this SDG</li> </ul> </li> <li>SDG-survey-selected-journals.csv <ul> <li>Based on our previous result set of papers, we presented researchers the journals that are in the metadata of those papers, they selected journals they believe represent this SDG.</li> </ul> </li> <li>SDG-survey-unselected-journals.csv <ul> <li>As "selected-journals", this is the list of journals that respondents have not selected to represent this SDG.</li> </ul> </li> <li>SDG-survey-suggested-journals.csv <ul> <li>List of journals researchers suggest to use to find papers related to this SDG</li> </ul> </li> <li>SDG-survey-suggested-query.csv <ul> <li>List of query improvements researchers suggest to use to find papers related to this SDG</li> </ul> </li> </ul> Cite as: <blockquote> Survey data of "Mapping Research output to the SDGs" by Aurora Universities Network (AUR) <a href="http://doi.org/10.5281/zenodo.3798385">doi:10.5281/zenodo.3798385</a> </blockquote> Attribute as: <blockquote> Survey data of "Mapping Research output to the SDGs" by Aurora Universities Network (AUR); Alessandro Arienzo (UNA); Roberto Delle Donne (UNA); Ignasi Salvadó Estivill (URV); José Luis González Ugarte (URV); Didier Vercueil (UGA); Nykohla Strong (UAB); Eike Spielberg (UDE); Felix Schmidt (UDE); Linda Hasse (UDE); Ane Sesma (UEA); Baldvin Zarioh (UIC); Friedrich Gaigg (UIN); René Otten (VUA); Nicolien van der Grijp (VUA); Yasin Gunes (VUA); Peter van den Besselaar (VUA); Joeri Both (VUA); Maurice Vanderfeesten (VUA); is licensed under a Creative Commons Attribution 4.0 International License. <a href="https://aurora-network.global/project/sdg-analysis-bibliometrics-relevance/">https://aurora-network.global/project/sdg-analysis-bibliometrics-relevance/</a> </blockquote>

opencc-by-4.0Apr 2024View details →

Supporting Material: Scientific Literature on the Sustainable Development Goals (SDGs). Scopus - May 2022

This is a supplementary dataset for an article analysing the scientific literature related to the Sustainable Development Goals (SDGs) using Scopus-indexed journals.  Data were retrieved in May 2022. Scopus was searched in the Title, Abstract, and Keywords fields  looking for each of the 17 SDGs (search query example: TITLE-ABS-KEY (“SDG1” or "SDG 1"). The dataset includes the following information for each of the 4808 scientific publication: ID: an identificatory alphanumerical number given by the authors Primary SDG: the main SDG the document focus on (MULTIPLE in case of more than one, ALL in case of all the SDGs) Year: Year of publication Title: title of the publication Abstract: abstract of the publication Index keywords: keywords of the publication

opencc-by-4.0Jun 2022View details →

A comparison of different methods of identifying publications related to the United Nations Sustainable Development Goals: Case Study of SDG 13: Climate Action

This data set pertains to the following research article: Purnell, P.J. (2022) A comparison of different methods of identifying publications related to the United Nations Sustainable Development Goals: Case Study of SDG 13 – Climate Action. arXiv:2201.02006

opencc-zeroJun 2024View details →

Dataset: iShares MSCI Global Sustainable Development Goals ETF (SDG) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

opencc-by-4.0Apr 2023View details →

Data for 'A multi‐methods approach for assessing how conserving biodiversity interacts with other sustainable development goals in Nepal'

Title: Data for 'A multi-methods approach for assessing how conserving biodiversity interacts with other sustainable development goals in Nepal' Recommended citation: Adhikari, B., Urbach, D., Chettri, N., Sharma, E., Breu, T., Geschke, J., Fischer, M. & Prescott, G. W. (2023) A multi-methods approach for assessing how conserving biodiversity interacts with other sustainable development goals in Nepal. Sustainable Development https://doi.org/10.1002/sd.2582 R code available at: https://github.com/biraj-ad/SDGIntearctions_Nepal_2023 Principal Investigator: Graham W Prescott (graham.prescott.research@gmail.com) Authors: Biraj Adhikari (biraj.adhikari@unibe.ch, ORCID: 0000-0002-4260-8706) Davnah Urbach (davnah.payne@unibe.ch, ORCID: 0000-0001-9170-7834) Nakul Chettri (nakul.chettri@icimod.org, ORCID: 0000-0002-3338-8879) Eklabya Sharma (eklabya.sharma11@gmail.com, ORCID:0000-0003-3089-8838) Thomas Breu (thomas.breu@unibe.ch, ORCID: 0000-0003-2348-504X) Jonas Geschke (jonas.geschke@unibe.ch, ORCID: 0000-0002-5654-9313) Markus Fischer (markus.fischer@ips.unibe.ch, ORCID: 0000-0002-5589-5900) Graham W Prescott (graham.prescott.research@gmail.com, ORCID:0000-0001-5123-514X) Date of data collection: August 2021 - June 2022 Location of data collection: Kathmandu Date of final release: Data Overview: There are two excel files. Each excel sheet is also uploaded as a separate .csv file. 1. 'Alldata.xls': This  excel file contains data collected for three independent methods used to develop the study. There are four sheets in this excel workbook. The first three sheet pertains to data collected for the seven-point SDG interactions score, correlation, and expert elicitation method. The last sheet is a derivative of the first three methods, which contains synthesized information of data. Sheet overview: method1: This sheet relates to the data collected through online expert survey using Kobo Toolbox. Description of columns: institution -> institutional affiliation of the participant inst_cat -> categorization of the institution into Intergovernmental Organization (IGO), Non-governmental Organization (NGO), Academia, and Government. exp_years -> experience (in years) of the participant in the conservation sector of Nepal sdgout and sdgin -> The SDG that the participant was randomly assigned to rate its outgoing and incoming interaction with SDG 15 respectively outscore -> the outgoing interaction score assigned by the participant, consisting of values between -3 (Cancelling) to +3 (Indivisible) conf_out -> the degree of confidence of the participant in their answer to the outgoing interaction score text_out -> (optional) a description of why the participant gave that outgoing interaction score inscore ->  the incoming interaction score assigned by the participant, consisting of values between -3 (Cancelling) to +3 (Indivisible) conf_in -> the degree of confidence of the participant in their answer to the incoming interaction score text_in -> (optional) a description of why the participant gave that incoming interaction score method2: This sheet relates to the data collected for correlation analysis. This includes time-series data of SDG indicators for Nepal obtained from the Global SDG Indicators Database (https://unstats.un.org/sdgs/indicators/database/). Description of columns: Year -> Year when the indicator was measured forest_cover -> Forest cover as a percent of total area (%) kba_freshwater -> Average proportion of Freshwater Key Biodiversity Areas (KBAs) covered by protected areas (%) kba_terrestiral -> Average proportion of Terrestrial Key Biodiversity Areas (KBAs) covered by protected areas (%) redlist -> Red List Index undernourishment -> Prevalence of undernourishment (%) food_insecurity -> Prevalence of moderate or severe food insecurity in the adult population (%) death_chronic -> Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease (probability) suicide -> Suicide mortality rate (deaths per 100,000 population) uhc_index -> Universal health coverage (UHC) service coverage index f_parliament -> Proportion of seats held by women in national parliaments (% of total number of seats) safewater -> Proportion of population using safely managed drinking water services (%) electricity -> Proportion of population with access to electricity, by urban/rural (%) cleanfuel -> Proportion of population with primary reliance on clean fuels and technology (%) renewable -> Renewable energy share in the total final energy consumption (%) gdp_capita -> Annual growth rate of real GDP per capita (%) manu_value -> Manufacturing value added as a proportion of GDP (%) consumption_gdp -> Domestic material consumption per unit of GDP (kilograms per constant 2010 United States dollars) consumption_all -> Domestic material consumption (tonnes) consumption_cap -> Domestic material consumption per capita (tonnes) death_missing -> Number of deaths, missing persons and persons affected by disaster per 100,000 people local_drr -> Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies (%) method3: This sheet relates to the data collected through key informant interviews. We obtained the count of interactions by coding interview responses as outgoing or incoming co-benefits or trade-offs in MaxQDA. The sheet summarizes the count for type of interaction (co-benefit or trade-off) for each SDG. allmethods: This sheet summarizes the proportion of co-benefits or trade-offs uncovered by each method for each SDG. 2. 'ForSynthesistable.xlsx': This excel file synthesizes goal level interactions for the three methods in three separate sheets. We use this table to develop figure 6 of the Manuscript.

opencc-by-4.0Aug 2022View details →

Trade-offs between Sustainable Development Goals in carbon capture and utilisation

Dataset associated with the publication "Trade-offs between Sustainable Development Goals in carbon capture and utilisation" by Iasonas Ioannou, Ángel Galán-Martín, Javier Pérez-Ramírez, and Gonzalo Guillén-Gosálbez, available at <a href="https://doi.org/10.1039/D2EE01153K">https://doi.org/10.1039/D2EE01153K</a>. The dataset includes the numeric data required to plot all the figures embedded in the main manuscript.

opencc-by-4.0Mar 2020View details →

Values and Sustainable Development Goals

Series of Lego Serious Play sessions to train students and potential entrepreneurs to use Lego Serious Play to ideate and propose innovative perspectives.

opencc-by-4.0Jan 2024View details →

Supplementary material to the study: "Understanding the interactions between biowaste valorization and selected Sustainable Development Goals: insights from an early transition stage"

This file includes supplementary material for the journal article: "Understanding the interactions between biowaste valorization and selected Sustainable Development Goals: insights from a national context at an early transition stage". The PDF file contains the interview guide, while the .xlsx file contains all other supplementary material.

opencc-by-4.0Dec 2023View details →

A Refined Supply-demand Framework to Quantify variability in Ecosystem Services Related to Surface Water in support of Sustainable Development Goals

The file named 'Results' stores the data produced in this study. The file named 'Scripts' stores the python codes used in this study. The file named 'Software' stores the software installation package (Windows 64-bit system). The file named '3basin' stores the shapefile data of Level 3 basin in Xinjiang.

opencc-by-4.0Mar 2024View details →

Datos de la investigación: La voz del rehén: adapting creative sustainable development goal-based physical-artístico project during COVID pandemic situation

This article assesses the effects on creativity of an interdisciplinary physicall-artistic programme and their effects during pandemic context. The sample comprised 97 individuals –54 men and 43 women– with an average age of 21.37±1.56, all of whom were studying a Primary Education Teacher Training Degree at Zaragoza University –Spain–. It was a pre-experimental study with measures pre-post programme –adapted to pandemic situation, replacing the acrosport content to skipping rope, juggling and aerial dance–. The creative skills assessment instrument was the PIC-A. A Student’s T-test and ANOVA test were performed to discover significant differences in pre-post in both academic years. Results showed that: i) participants obtained significant improvements in total score of creativity in both academic years; ii) the adaptations made to the intervention programme during the pandemic year were effective in developing creativity. These studies confirm the importance of incorporating interdisciplinary creativity programmes into the university system.

opencc-by-2.0Nov 2021View details →

Developing the System of Radiological Protection to Enhance Its Contribution to the UN Sustainable Development Goals

The system of radiological protection has evolved since the publication of the ICRP’s first set of recommendations in 1959. It has enabled the beneficial uses of radiation and radioactive substances while protecting humans from their harmful tissue reactions and carcinogenic (and other stochastic) effects. The system has arguably been world leading in the protection of humans from carcinogens. Since the publication of the most recent ICRP recommendations in 2007 the pace of change in global socio-economic challenges and environmental degradation has accelerated. We face climate, biodiversity and pollution emergencies as well as global health crises such as Covid-19 and increases in non-communicable diseases such as cancer. In addressing the global issues we face it is therefore more important than ever to take an integrated approach to balancing social, environmental and economic risks and impacts. In the context of radiological protection it is vital that our efforts to reduce or control radiation risks consider the wider consequences of those efforts if we are to avoid causing more harm than good. The United Nations Sustainable Development Goals (SDGs) are an ideal framework for facilitating a balanced approach to socio-economic development and environmental protection and enhancement.  They recognise that ending poverty and other deprivations must go hand-in-hand with improving health and education, reducing inequality, and spurring economic growth – all while tackling climate change and preserving our environment. The ICRP system of radiological protection is based on three fundamental principles: justification, optimisation and the limitation of radiation exposure. The principle of justification requires that any decision that changes the amount of radiation exposure should do more good than harm. Optimisation requires that radiation exposure should be kept as low as reasonably achievable, taking into account economic and societal factors. Optimisation is not the minimisation of radiation exposure rather the maximising of the net benefit relative to the radiation exposure. This paper explores how the system of radiological protection contributes to the delivery of sustainable development and considers how the SDGs might be taken into account in its further development and application to ensure that the system is fit for the 21st century.

opencc-by-4.0Jan 2023View details →

The Anthropocene and the Sustainable Development Goals: Key elements in geography higher education? Study Dataset.

The document contains the Dataset of a study "The Anthropocene and the Sustainable Development Goals: Key elements in geography higher education? "